"""simple docstring"""



import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__:Optional[int]									=			logging.getLogger(__name__)


SCREAMING_SNAKE_CASE__:Union[str, Any]									=			"""Hello world! cécé herlolip"""


SCREAMING_SNAKE_CASE__:Optional[int]									=			namedtuple(
    """BertAbsConfig""",
    [
        """temp_dir""",
        """large""",
        """use_bert_emb""",
        """finetune_bert""",
        """encoder""",
        """share_emb""",
        """max_pos""",
        """enc_layers""",
        """enc_hidden_size""",
        """enc_heads""",
        """enc_ff_size""",
        """enc_dropout""",
        """dec_layers""",
        """dec_hidden_size""",
        """dec_heads""",
        """dec_ff_size""",
        """dec_dropout""",
    ],
)



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    BertAbsConfig(
           temp_dir="."   ,    finetune_bert=a   ,    large=a   ,    share_emb=a   ,    use_bert_emb=a   ,    encoder="bert"   ,    max_pos=5_1_2   ,    enc_layers=6   ,    enc_hidden_size=5_1_2   ,    enc_heads=8   ,    enc_ff_size=5_1_2   ,    enc_dropout=0.2   ,    dec_layers=6   ,    dec_hidden_size=7_6_8   ,    dec_heads=8   ,    dec_ff_size=2_0_4_8   ,    dec_dropout=0.2   ,    )
       __a       =    torch.load(a   ,    lambda a   ,    a				: storage				)
       __a       =    AbsSummarizer(a   ,    torch.device("cpu"				)   ,    a				)
       original.eval()

       __a       =    BertAbsSummarizer(a   ,    torch.device("cpu"				)				)
       new_model.eval()

       # -------------------
       # Convert the weights
       # -------------------

       logging.info("convert the model"				)
       new_model.bert.load_state_dict(original.bert.state_dict()				)
       new_model.decoder.load_state_dict(original.decoder.state_dict()				)
       new_model.generator.load_state_dict(original.generator.state_dict()				)

       # ----------------------------------
       # Make sure the outpus are identical
       # ----------------------------------

       logging.info("Make sure that the models' outputs are identical"				)
       __a       =    BertTokenizer.from_pretrained("bert-base-uncased"				)

       # prepare the model inputs
       __a       =    tokenizer.encode("This is sample éàalj'-."				)
       encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(a				))				)
       __a       =    torch.tensor(a				).unsqueeze(0				)
       __a       =    tokenizer.encode("This is sample 3 éàalj'-."				)
       decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(a				))				)
       __a       =    torch.tensor(a				).unsqueeze(0				)

       # failsafe to make sure the weights reset does not affect the
       # loaded weights.
       assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight				)				) == 0

       # forward pass
       __a       =    encoder_input_ids
       __a       =    decoder_input_ids
       __a       =    __a       =    None
       __a       =    None
       __a       =    __a       =    None
       __a       =    __a       =    None
       __a       =    None

       # The original model does not apply the geneator layer immediatly but rather in
       # the beam search (where it combines softmax + linear layer). Since we already
       # apply the softmax in our generation process we only apply the linear layer here.
       # We make sure that the outputs of the full stack are identical
       __a       =    original(a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a				)[0]
       __a       =    original.generator(a				)

       __a       =    new_model(
           a   ,    a   ,    a   ,    a   ,    a				)[0]
       __a       =    new_model.generator(a				)

       __a       =    torch.max(torch.abs(output_converted_model - output_original_model				)				).item()
       print("Maximum absolute difference beween weights: {:.2f}".format(a				)				)
       __a       =    torch.max(torch.abs(output_converted_generator - output_original_generator				)				).item()
       print("Maximum absolute difference beween weights: {:.2f}".format(a				)				)

       __a       =    torch.allclose(a   ,    a   ,    atol=1E-3				)
       if are_identical:
              logging.info("all weights are equal up to 1e-3"				)
       else:
              raise ValueError("the weights are different. The new model is likely different from the original one."				)

       # The model has been saved with torch.save(model) and this is bound to the exact
       # directory structure. We save the state_dict instead.
       logging.info("saving the model's state dictionary"				)
       torch.save(
           new_model.state_dict()   ,    "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[Any]									=			argparse.ArgumentParser()
   parser.add_argument(
       """--bertabs_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help="""Path the official PyTorch dump.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the output PyTorch model.""",
   )
   SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )

"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Union[str, Any]							  =						"""sew-d"""


 def __init__( self    ,		lowerCamelCase=32    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase=2    ,		lowerCamelCase=512    ,		lowerCamelCase=256    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=("p2c", "c2p")    ,		lowerCamelCase="layer_norm"    ,		lowerCamelCase="gelu_python"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-7    ,		lowerCamelCase=1E-5    ,		lowerCamelCase="group"    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,		lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,		lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,		lowerCamelCase=False    ,		lowerCamelCase=128    ,		lowerCamelCase=16    ,		lowerCamelCase=True    ,		lowerCamelCase=0.05    ,		lowerCamelCase=10    ,		lowerCamelCase=2    ,		lowerCamelCase=0.0    ,		lowerCamelCase=10    ,		lowerCamelCase=0    ,		lowerCamelCase="mean"    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=256    ,		lowerCamelCase=0    ,		lowerCamelCase=1    ,		lowerCamelCase=2    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase    ,		pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase						)
        __a       =    hidden_size
        __a       =    feat_extract_norm
        __a       =    feat_extract_activation
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    conv_bias
        __a       =    num_conv_pos_embeddings
        __a       =    num_conv_pos_embedding_groups
        __a       =    len(self.conv_dim						)
        __a       =    num_hidden_layers
        __a       =    intermediate_size
        __a       =    squeeze_factor
        __a       =    max_position_embeddings
        __a       =    position_buckets
        __a       =    share_att_key
        __a       =    relative_attention
        __a       =    norm_rel_ebd
        __a       =    list(lowerCamelCase						)
        __a       =    hidden_act
        __a       =    num_attention_heads
        __a       =    hidden_dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    feat_proj_dropout
        __a       =    final_dropout
        __a       =    layer_norm_eps
        __a       =    feature_layer_norm_eps
        __a       =    initializer_range
        __a       =    vocab_size

        if (
            (len(self.conv_stride						) != self.num_feat_extract_layers)
            or (len(self.conv_kernel						) != self.num_feat_extract_layers)
            or (len(self.conv_dim						) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a       =    apply_spec_augment
        __a       =    mask_time_prob
        __a       =    mask_time_length
        __a       =    mask_time_min_masks
        __a       =    mask_feature_prob
        __a       =    mask_feature_length
        __a       =    mask_feature_min_masks

        # ctc loss
        __a       =    ctc_loss_reduction
        __a       =    ctc_zero_infinity

        # sequence classification
        __a       =    use_weighted_layer_sum
        __a       =    classifier_proj_size






 @property
 def 						a__   ( self						):
        return functools.reduce(operator.mul    ,		self.conv_stride    ,		1						)
